Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

Comparison of lung image quality between CT Ark and Brilliance 64 CT during COVID-19.

AIM: This study is to compare the lung image quality between shelter hospital CT (CT Ark) and ordinary CT scans (Brilliance 64) scans. METHODS: The patients who received scans with CT Ark or Brilliance 64 CT were enrolled. Their lung images were divided into two groups according to the scanner. The objective evaluation methods of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used. The subjective evaluation methods including the evaluation of the fine structure under the lung window and the evaluation of the general structure under the mediastinum window were compared. Kappa method was used to assess the reliability of the subjective evaluation. The subjective evaluation results were analyzed using the Wilcoxon rank sum test. SNR and CNR were tested using independent sample t tests. RESULTS: There was no statistical difference in somatotype of enrolled subjects. The Kappa value between the two observers was between 0.68 and 0.81, indicating good consistency. For subjective evaluation results, the rank sum test P value of fine structure evaluation and general structure evaluation by the two observers was ≥ 0.05. For objective evaluation results, SNR and CNR between the two CT scanners were significantly different (P<0.05). Notably, the absolute values ​​of SNR and CNR of the CT Ark were larger than Brilliance 64 CT scanner. CONCLUSION: CT Ark is fully capable of scanning the lungs of the COVID-19 patients during the epidemic in the shelter hospital.

AANet: Adaptive Attention Network for COVID-19 Detection From Chest X-Ray Images.

Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunately, identifying COVID-19 from the CXR images is challenging as its radiographic features have a variety of complex appearances, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. To solve this problem, we propose an adaptive attention network (AANet), which can adaptively extract the characteristic radiographic findings of COVID-19 from the infected regions with various scales and appearances. It contains two main components: an adaptive deformable ResNet and an attention-based encoder. First, the adaptive deformable ResNet, which adaptively adjusts the receptive fields to learn feature representations according to the shape and scale of infected regions, is designed to handle the diversity of COVID-19 radiographic features. Then, the attention-based encoder is developed to model nonlocal interactions by self-attention mechanism, which learns rich context information to detect the lesion regions with complex shapes. Extensive experiments on several public datasets show that the proposed AANet outperforms state-of-the-art methods.

The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT.

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients (p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs (p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

Chest Computed Tomography (CT) Severity Scales in COVID-19 Disease: A Validation Study.

BACKGROUND Imaging-based quantitative assessment of lung lesions plays a key role in patient triage and therapeutic decision-making processes. The aim of our study was to validate the Total Severity Score (TSS), Chest Computed Tomography Score (CT-S), and Chest CT Severity Score (CT-SS) scales, which were used to assess the extent of lung inflammation in patients with SARS-CoV-2 infection in terms of interobserver agreement and the correlation of scores with patient clinical condition on the day of the study. MATERIAL AND METHODS A total of 77 chest CT scans collected from 77 consecutive patients hospitalized because of SARS-CoV-2 were included. The scans were assessed independently by 2 radiologists aware of the patients' positive results of RT-PCR tests. Each chest CT was assessed according to the 3 scales. To assess the interobserver agreement of CT scan assessments, Cohen's k and intraclass correlation coefficient (ICC) were calculated. RESULTS For the overall assessment, the k was 0.944 and the ICC was 0.948 for the TSS; the kappa was 0.909 and the ICC was 0.919 for the CT-S; and the k was 0.888 and the ICC was 0.899 for the CT-SS. The CT-SS (r=0.627 for Radiologist 1 and r=0.653 for Radiologist 2) revealed the strongest positive correlation with the patient clinical condition as expressed using the Modified Early Warning Score. CONCLUSIONS The interobserver agreement for the 3 evaluated scales was very good. The CT-SS was found to have the strongest positive relationship with the Modified Early Warning Score.

RSNA-STR-ACR Consensus Statement for COVID-19 CT Patterns: Interreader Agreement in 240 Consecutive Patients and Association With RT-PCR Status.

PURPOSE: The aim of this study was to study interreader agreement of the RSNA-STR-ACR (Radiological Society of North America/Society of Thoracic Radiology/American College of Radiology) consensus statement on reporting chest computed tomography (CT) findings related to COVID-19 on a sample of consecutive patients confirmed with reverse transcriptase-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2. MATERIALS AND METHODS: This institutional review board-approved retrospective study included 240 cases with a mean age of 47.6 ± 15.9 years, ranging from 20 to 90 years, who had a chest CT and RT-PCR performed. Computed tomography images were independently analyzed by 2 thoracic radiologists to identify patterns defined by the RSNA-STR-ACR consensus statement, and concordance was determined with weighted κ tests. Also, CT findings and CT severity scores were tabulated and compared. RESULTS: Of the 240 cases, 118 had findings on CT. The most frequent on the RT-PCR-positive group were areas of ground-glass opacities (80.5%), crazy-paving pattern (32.2%), and rounded pseudonodular ground-glass opacities (22.9%). Regarding the CT patterns, the most frequent in the RT-PCR-positive group was typical in 75.9%, followed by negative in 17.1%. The interreader agreement was 0.90 (95% confidence interval, 0.80-0.96) in this group. The CT severity score had a mean difference of -0.07 (95% confidence interval, -0.48 to 0.34) among the readers, showing no significant differences regarding visual estimation. CONCLUSIONS: The RSNA-STR-ACR consensus statement on reporting chest CT patterns for COVID-19 presents a high interreader agreement, with the typical pattern being more frequently associated with RT-PCR-positive examinations.

Thoracic imaging tests for the diagnosis of COVID-19.

BACKGROUND: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies. OBJECTIVES: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions. SELECTION CRITERIA: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates. DATA COLLECTION AND ANALYSIS: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity. AUTHORS' CONCLUSIONS: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices.

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia.

Segmentation is a complex task, faced by radiologists and researchers as radiomics and machine learning grow in potentiality. The process can either be automatic, semi-automatic, or manual, the first often not being sufficiently precise or easily reproducible, and the last being excessively time consuming when involving large districts with high-resolution acquisitions. A high-resolution CT of the chest is composed of hundreds of images, and this makes the manual approach excessively time consuming. Furthermore, the parenchymal alterations require an expert evaluation to be discerned from the normal appearance; thus, a semi-automatic approach to the segmentation process is, to the best of our knowledge, the most suitable when segmenting pneumonias, especially when their features are still unknown. For the studies conducted in our institute on the imaging of COVID-19, we adopted 3D Slicer, a freeware software produced by the Harvard University, and combined the threshold with the paint brush instruments to achieve fast and precise segmentation of aerated lung, ground glass opacities, and consolidations. When facing complex cases, this method still requires a considerable amount of time for proper manual adjustments, but provides an extremely efficient mean to define segments to use for further analysis, such as the calculation of the percentage of the affected lung parenchyma or texture analysis of the ground glass areas.

A proposed ABCD scoring system for better triage of patients with COVID-19: Use of clinical features and radiopathological findings.

BACKGROUND AND AIMS: Currently there are limited tools available for triage of patients with COVID -19. We propose a new ABCD scoring system for patients who have been tested positive for COVID-19. METHODS: The ABCD score is for patients who have been tested positive for COVID-19 and admitted in a hospital. This score includes age of the patient, blood tests included leukopenia, lymphocytopenia, CRP level, LDH level,D-Dimer, Chest radiograph and CT Scan, Comorbidities and Dyspnea. RESULTS: The triage score had letters from alphabets which included A, B, C, D. The score was developed using these variables which outputs a value from 0 to 1. We had used the code according to traffic signal system; green(mild), yellow moderate) and red(severe). The suggestions for mild (green)category: symptomatic treatment in ward, in moderate (yellow) category: active treatment, semi critical care and oxygen supplementation, in severe (red) category: critical care and intensive care. CONCLUSIONS: This study is, to our knowledge, is the first scoring tool that has been prepared by Indian health care processional's and used alphabets A, B,C,D as variables for evaluation of admitted patients with COVID-19. This triage tool will be helpful in better management of patients with COVID-19. This score component includes clinical and radiopathological findings.A multi-centre study is required to validate all available scoring systems.

The utility of chest computed tomography (CT) and RT-PCR screening of asymptomatic patients for SARS-CoV-2 prior to semiurgent or urgent hospital procedures.

OBJECTIVE: Presently, evidence guiding clinicians on the optimal approach to safely screen patients for coronavirus disease 2019 (COVID-19) to a nonemergent hospital procedure is scarce. In this report, we describe our experience in screening for SARS-CoV-2 prior to semiurgent and urgent hospital procedures. DESIGN: Retrospective case series. SETTING: A single tertiary-care medical center. PARTICIPANTS: Our study cohort included patients ≥18 years of age who had semiurgent or urgent hospital procedures or surgeries. METHODS: Overall, 625 patients were screened for SARS-CoV-2 using a combination of phone questionnaire (7 days prior to the anticipated procedure), RT-PCR and chest computed tomography (CT) between March 1, 2020, and April 30, 2020. RESULTS: Of the 625 patients, 520 scans (83.2%) were interpreted as normal; 1 (0.16%) had typical features of COVID-19; 18 scans (2.88%) had indeterminate features of COVID-19; and 86 (13.76%) had atypical features of COVID-19. In total, 640 RT-PCRs were performed, with 1 positive result (0.15%) in a patient with a CT scan that yielded an atypical finding. Of the 18 patients with chest CTs categorized as indeterminate, 5 underwent repeat negative RT-PCR nasopharyngeal swab 1 week after their initial swab. Also, 1 patient with a chest CT categorized as typical had a follow-up repeat negative RT-PCR, indicating that the chest CT was likely a false positive. After surgery, none of the patients developed signs or symptoms suspicious of COVID-19 that would indicate the need for a repeated RT-PCR or CT scan. CONCLUSION: In our experience, chest CT scanning did not prove provide valuable information in detecting asymptomatic cases of SARS-CoV-2 (COVID-19) in our low-prevalence population.

A Review on Applicable and Available Paraclinical Methods for Diagnosis of Coronavirus Disease-19.

BACKGROUND: The recent outbreak by a novel coronavirus originated from Wuhan, China in 2019, and is progressively spreading to other countries. Timely diagnosis of the coronavirus disease 2019 (COVID-19) improves the survival of the patients and also prevents the transmission of the infection. In this study, we reviewed the applicable and available methods for the diagnosis of COVID-19. METHODS: For the review, we systematically searched Web of Science, PubMed, and Iranian articles that were published about COVID-19 diagnostic methods with a combination of the key terms: laboratory, radiological, tests, coronavirus. RESULTS: Although the current gold standard diagnostic test for this virus is real-time reverse-transcriptase polymerase chain reaction (RT-PCR), the occasional false-negative and the low sensitivity of the test should not be underestimated. A chest computed tomography (CT) scan is another diagnostic test for COVID-19, with higher sensitivity but low specificity. A combination of sensitive RT-PCR with a chest CT scan together with the clinical features are highly recommended for the proper diagnosis. Notably, there are some other sensitive and low-cost tests for evaluation of COVID-19 infection, but their validation should be approved. CONCLUSION: Since early and accurate diagnosis of the viral disease could improve the survival rate of the patients, and halt the transmission chain, it is not surprising that tremendous attempts should be made to reduce the limitations of the tests leading to the false-negative results and to find a rapid test for the diagnosis of COVID-19.

Electrical impedance tomography to titrate positive end-expiratory pressure in COVID-19 acute respiratory distress syndrome.

RATIONALE: Patients with coronavirus disease-19-related acute respiratory distress syndrome (C-ARDS) could have a specific physiological phenotype as compared with those affected by ARDS from other causes (NC-ARDS). OBJECTIVES: To describe the effect of positive end-expiratory pressure (PEEP) on respiratory mechanics in C-ARDS patients in supine and prone position, and as compared to NC-ARDS. The primary endpoint was the best PEEP defined as the smallest sum of hyperdistension and collapse. METHODS: Seventeen patients with moderate-to-severe C-ARDS were monitored by electrical impedance tomography (EIT) and evaluated during PEEP titration in supine (n = 17) and prone (n = 14) position and compared with 13 NC-ARDS patients investigated by EIT in our department before the COVID-19 pandemic. RESULTS: As compared with NC-ARDS, C-ARDS exhibited a higher median best PEEP (defined using EIT as the smallest sum of hyperdistension and collapse, 12 [9, 12] vs. 9 [6, 9] cmH2O, p < 0.01), more collapse at low PEEP, and less hyperdistension at high PEEP. The median value of the best PEEP was similar in C-ARDS in supine and prone position: 12 [9, 12] vs. 12 [10, 15] cmH2O, p = 0.59. The response to PEEP was also similar in C-ARDS patients with higher vs. lower respiratory system compliance. CONCLUSION: An intermediate PEEP level seems appropriate in half of our C-ARDS patients. There is no solid evidence that compliance at low PEEP could predict the response to PEEP.

Diagnostic performance of chest computed tomography during the epidemic wave of COVID-19 varied as a function of time since the beginning of the confinement in France.

OBJECTIVE: To evaluate the diagnostic performance of the initial chest CT to diagnose COVID-19 related pneumonia in a French population of patients with respiratory symptoms according to the time from the onset of country-wide confinement to better understand what could be the role of the chest CT in the different phases of the epidemic. MATERIAL AND METHOD: Initial chest CT of 1064 patients with respiratory symptoms suspect of COVID-19 referred between March 18th, and May 12th 2020, were read according to a standardized procedure. The results of chest CTs were compared to the results of the RT-PCR. RESULTS: 546 (51%) patients were found to be positive for SARS-CoV2 at RT-PCR. The highest rate of positive RT-PCR was during the second week of confinement reaching 71.9%. After six weeks of confinement, the positive RT-PCR rate dropped significantly to 10.5% (p<0.001) and even 2.2% during the two last weeks. Overall, CT revealed patterns suggestive of COVID-19 in 603 patients (57%), whereas an alternative diagnosis was found in 246 patients (23%). CT was considered normal in 215 patients (20%) and inconclusive in 1 patient. The overall sensitivity of CT was 88%, specificity 76%, PPV 79%, and NPV 85%. At week-2, the same figures were 89%, 69%, 88% and 71% respectively and 60%, 84%, 30% and 95% respectively at week-6. At the end of confinement when the rate of positive PCR became extremely low the sensitivity, specificity, PPV and NPV of CT were 50%, 82%, 6% and 99% respectively. CONCLUSION: At the peak of the epidemic, chest CT had sufficiently high sensitivity and PPV to serve as a first-line positive diagnostic tool but at the end of the epidemic wave CT is more useful to exclude COVID-19 pneumonia.

The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.

With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. Moreover, it is supplemented with a carefully defined split. The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models.

The role of a chest computed tomography severity score in coronavirus disease 2019 pneumonia.

The chest computed tomography (CT) characteristics of coronavirus disease 2019 (COVID-19) are important for diagnostic and prognostic purposes. The aim of this study was to investigate chest CT findings in COVID-19 patients in order to determine the optimal cut-off value of a CT severity score that can be considered a potential prognostic indicator of a severe/critical outcome.The CT findings were evaluated by means of a severity score that included the extent (0-4 grading scale) and nature (0-4 grading scale) of CT abnormalities. The images were evaluated at 3 levels bilaterally. A receiver operating characteristics (ROC) curve was used to identify the optimal score (Youden's index) predicting severe/critical COVID-19.The study involved 165 COVID-19 patients (131 men [79.4%] and 34 women [20.6%] with a mean age of 61.5 ±â€Š12.5 years), of whom 30 (18.2%) had severe/critical disease and 135 (81.8%) mild/typical disease. The most frequent CT finding was bilateral predominantly subpleural and basilar airspace changes, with more extensive ground-glass opacities than consolidation. CT findings of consolidation, a crazy-paving pattern, linear opacities, air bronchogram, and extrapulmonary lesions correlated with severe/critical COVID-19. The mean CT severity score was 63.95 in the severe/critical group, and 35.62 in the mild/typical group (P < .001). ROC curve analysis showed that a CT severity score of 38 predicted the development of severe/critical symptoms.A CT severity score can help the risk stratification of COVID-19 patients.

Dose-optimised chest computed tomography for diagnosis of Coronavirus Disease 2019 (COVID-19) - Evaluation of image quality and diagnostic impact.

OBJECTIVES: The detection of Coronavirus Disease 2019 (COVID-19) by reverse transcription polymerase chain reaction (RT-PCR) has varying sensitivity. Computed tomography (CT) of the chest can verify infection in patients with clinical symptoms and a negative test result, accelerating treatment and actions to prevent further contagion. However, CT employs ionising radiation. The purpose of this study was to evaluate protocol settings, associated radiation exposure, image quality and diagnostic performance of a low-dose CT protocol in a university hospital setting. MATERIALS AND METHODS: Chest CT examinations were performed on a single scanner (Somatom Definition Edge, Siemens Healthineers, Germany) in 105 symptomatic patients (60 male, 45 female). Images were evaluated with regard to protocol parameters, image quality, radiation exposure and diagnostic accuracy. Serial RT-PCR served as the standard of reference. Based on this reference standard sensitivity, specificity, positive and negative predictive values of CT with 95% confidence interval were calculated. RESULTS: The mean effective dose was 1.3 ± 0.4 mSv (0.7-2.9 mSv) for the patient cohort (mean age 66.6 ± 16.7 years (19-94 years), mean body mass index (BMI) 26.6 ± 5.3 kg m-2 (16-46 kg/m2)). A sensitivity of 100 [95% CI: 82-100]%, a specificity of 78 [95% CI: 68-86]%, a positive predictive value of 50 [95% CI: 33-67]% and a negative predictive value of 100 [95% CI: 95-100]% were obtained. No COVID-19 diagnoses were missed by CT. Image noise did not strongly correlate with BMI or patient diameter and was rated as average. CONCLUSIONS: We presented a robust imaging procedure with a chest CT protocol for confident diagnosis of COVID-19. Even for an overweight patient cohort, an associated radiation exposure of only 1.3 ± 0.4 mSv was achieved with sufficient diagnostic quality to exclude COVID-19.

Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room.

After December 2019 outbreak in China, the novel Coronavirus infection (COVID-19) has very quickly overflowed worldwide. Infection causes a clinical syndrome encompassing a wide range of clinical features, from asymptomatic or oligosymptomatic course to acute respiratory distress and death. In a very recent work we preliminarily observed that several laboratory tests have been shown as characteristically altered in COVID-19. We aimed to use the Corona score, a validated point-based algorithm to predict the likelihood of COVID-19 infection in patients presenting at the Emergency rooms. This approach combines chest images-relative score and several laboratory parameters to classify emergency room patients. Corona score accuracy was satisfactory, increasing the detection of positive patients' rate.

CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for use in the standardized assessment of pulmonary involvement of COVID-19 on unenhanced chest CT images and to report its initial interobserver agreement and performance. Materials and Methods The Dutch Radiological Society developed CO-RADS based on other efforts for standardization, such as the Lung Imaging Reporting and Data System or Breast Imaging Reporting and Data System. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients with moderate to severe symptoms of COVID-19. The system was evaluated by using 105 chest CT scans of patients admitted to the hospital with clinical suspicion of COVID-19 and in whom reverse transcription-polymerase chain reaction (RT-PCR) was performed (mean, 62 years ± 16 [standard deviation]; 61 men, 53 with positive RT-PCR results). Eight observers used CO-RADS to assess the scans. Fleiss κ value was calculated, and scores of individual observers were compared with the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared with results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss κ value was 0.47 (95% confidence interval [CI]: 0.45, 0.47), with the highest κ value for CO-RADS categories 1 (0.58, 95% CI: 0.54, 0.62) and 5 (0.68, 95% CI: 0.65, 0.72). The average AUC was 0.91 (95% CI: 0.85, 0.97) for predicting RT-PCR outcome and 0.95 (95% CI: 0.91, 0.99) for clinical diagnosis. The false-negative rate for CO-RADS 1 was nine of 161 cases (5.6%; 95% CI: 1.0%, 10%), and the false-positive rate for CO-RADS category 5 was one of 286 (0.3%; 95% CI: 0%, 1.0%). Conclusion The coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) is a categorical assessment scheme for pulmonary involvement of COVID-19 at unenhanced chest CT that performs very well in predicting COVID-19 in patients with moderate to severe symptoms and has substantial interobserver agreement, especially for categories 1 and 5. © RSNA, 2020 Online supplemental material is available for this article.